Using Kernel Discriminant Analysis and 2DGabor Local Features Fusion for Face Recognition

A novelty method of 2DGabor-KDA(kernel Fisher discriminant analysis) for face recognition is proposed. This involves convolving face images which are segmented into several sub-areas according to the five particular face parts are extracted through 2DGabor wavelet, average values are calculated from feature vectors gained from the corresponding pixel of each test sample and then the eigenvectors are gained, KDA which has been proved to be an effective approach for face recognition in dealing with complex and nonlinear face image variations is applied to kernel-process the gained eigenvectors , and then SVM (Support Vector Machine) is adopted to recognize the face images. The numerical experiments on face databases of ORL and YALE indicate that the proposed methods are efficient while retaining the same recognition accuracy.